Fetching the paper…
Reading the bibliography…
Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and statistical paradigms of cognition, has been an active research area of Artificial Intelligence (AI) for many years.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell
1901
Earlier work this paper cites.
M. Fischer, M. Balunovic, D. Drachsler-Cohen, T. Gehr, C. Zhang, and M. Vechev, “Dl2: Training and querying neural networks with logic,” in
1941
Earlier work this paper cites.
W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,”
1943
Earlier work this paper cites.
P. Kiparsky and J. F. Staal, “Syntactic and semantic relations in panini,”
1969
Earlier work this paper cites.
E. A. Felgenbaum, “The art of artificial intelligence: themes and case studies of knowledge engineering,” in
1977
Earlier work this paper cites.
J. Gu, H. Zhao, Z. Lin, S. Li, J. Cai, and M. Ling, “Scene graph generation with external knowledge and image reconstruction,” in
1978
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” California Univ San Diego La Jolla Inst for Cognitive Science, Tech. Rep., 1985
1985
Earlier work this paper cites.
J. A. Fodor and Z. W. Pylyshyn, “Connectionism and cognitive architecture: A critical analysis,”
1988
Earlier work this paper cites.
P. Smolensky, “On the proper treatment of connectionism,”
1988
Earlier work this paper cites.
J. Haugeland,
1989
Earlier work this paper cites.
G. G. Towell, J. W. Shavlik, M. O. Noordewier
1990
Earlier work this paper cites.
J. B. Pollack, “Recursive distributed representations,”
1990
Earlier work this paper cites.
S. Hölldobler, Y. Kalinke, F. W. Ki
1991
Earlier work this paper cites.
L. Shastri and V. Ajjanagadde, “From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony,”
1993
Earlier work this paper cites.
G. G. Towell and J. W. Shavlik, “Knowledge-based artificial neural networks,”
1994
Earlier work this paper cites.
P. S. Churchland and T. J. Sejnowski,
1994
Earlier work this paper cites.
T. A. Plate, “Holographic reduced representations,”
1995
Earlier work this paper cites.
G. A. Miller, “Wordnet: a lexical database for english,”
1995
Earlier work this paper cites.
P. M. Dung, “On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games,”
1995
Earlier work this paper cites.
T. W. Deacon, “The co-evolution of language and the brain,”
1997
Earlier work this paper cites.
T. M. Mitchell,
1997
Earlier work this paper cites.
A. Bondarenko, P. M. Dung, R. A. Kowalski, and F. Toni, “An abstract, argumentation-theoretic approach to default reasoning,”
1997
Earlier work this paper cites.
R. Khardon and D. Roth, “Learning to reason,”
1997
Earlier work this paper cites.
N. J. Roese, “Counterfactual thinking.”
1997
Earlier work this paper cites.
A. Garcez and G. Zaverucha, “The connectionist inductive learning and logic programming system,”
1999
Earlier work this paper cites.
I. Cloete and J. M. Zurada,
2000
Earlier work this paper cites.
A. Browne and R. Sun, “Connectionist inference models,”
2001
Earlier work this paper cites.
E. Dantsin, T. Eiter, G. Gottlob, and A. Voronkov, “Complexity and expressive power of logic programming,”
2001
Earlier work this paper cites.
A. S. d. Garcez, K. Broda, D. M. Gabbay
2002
Earlier work this paper cites.
M. R. Ryan, “Using abstract models of behaviours to automatically generate reinforcement learning hierarchies,” in
2002
Earlier work this paper cites.
L. G. Valiant, “Three problems in computer science,”
2003
Earlier work this paper cites.
S. Horst, “The computational theory of mind,” 2003
2003
Earlier work this paper cites.
W. F. Clocksin and C. S. Mellish,
2003
Earlier work this paper cites.
T. J. Bench-Capon, “Persuasion in practical argument using value-based argumentation frameworks,”
2003
Earlier work this paper cites.
S. Bader and P. Hitzler, “Dimensions of neural-symbolic integration – a structured survey,”
2005
Earlier work this paper cites.
A. S. D’Avila Garcez, D. M. Gabbay, and L. C. Lamb, “Value-based argumentation frameworks as neural-symbolic learning systems,”
2005
Earlier work this paper cites.
M. Richardson and P. Domingos, “Markov logic networks,”
2006
Earlier work this paper cites.
A. S. d. Garcez, L. C. Lamb, and D. M. Gabbay, “Connectionist computations of intuitionistic reasoning,”
2006
Earlier work this paper cites.
L. De Raedt, A. Kimmig, and H. Toivonen, “Problog: A probabilistic prolog and its application in link discovery.” in
2007
Earlier work this paper cites.
——, “Connectionist modal logic: Representing modalities in neural networks,”
2007
Earlier work this paper cites.
A. S. Garcez, L. C. Lamb, and D. M. Gabbay,
2009
Earlier work this paper cites.
S. J. Russell and P. Norvig,
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
Earlier work this paper cites.
P. Pagin and D. Westerståhl, “Compositionality i: Definitions and variants,”
2010
Earlier work this paper cites.
D. Kahneman,
2011
Earlier work this paper cites.
H. L. H. de Penning, A. S. d. Garcez, L. C. Lamb, and J.-J. C. Meyer, “A neural-symbolic cognitive agent for online learning and reasoning,” in
2011
Earlier work this paper cites.
M. Capobianco and G. R. Simari, “An argument-based multi-agent system for information integration,” in
2011
Earlier work this paper cites.
T. M. Janssen, “Compositionality: Its historic context,”
2012
Earlier work this paper cites.
Z. Szabó, “The case for compositionality,”
2012
Earlier work this paper cites.
V. Novák, I. Perfilieva, and J. Mockor,
2012
Earlier work this paper cites.
A. Graves and A. Graves, “Long short-term memory,”
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
J. Lee, V. Lifschitz, and F. Yang, “Action language bc: Preliminary report.” in
2013
Earlier work this paper cites.
J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in
2014
Earlier work this paper cites.
A. S. d. Garcez, D. M. Gabbay, and L. C. Lamb, “A neural cognitive model of argumentation with application to legal inference and decision making,”
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,”
2015
Earlier work this paper cites.
Á. Carrera and C. A. Iglesias, “A systematic review of argumentation techniques for multi-agent systems research,”
2015
Earlier work this paper cites.
J. Andreas, M. Rohrbach, T. Darrell, and D. Klein, “Neural module networks,” in
2016
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot
2016
Earlier work this paper cites.
M. Garnelo, K. Arulkumaran, and M. Shanahan, “Towards deep symbolic reinforcement learning,” in
2016
Earlier work this paper cites.
W. W. Cohen, “Tensorlog: A differentiable deductive database,”
2016
Earlier work this paper cites.
L. Serafini and A. d. Garcez, “Logic tensor networks: Deep learning and logical reasoning from data and knowledge,” in
2016
Earlier work this paper cites.
T. Demeester, T. Rocktäschel, and S. Riedel, “Lifted rule injection for relation embeddings,” in
2016
Earlier work this paper cites.
Z. Hu, X. Ma, Z. Liu, E. Hovy, and E. Xing, “Harnessing deep neural networks with logic rules,” in
2016
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in
2016
Earlier work this paper cites.
J. Andreas, M. Rohrbach, T. Darrell, and D. Klein, “Learning to compose neural networks for question answering,” in
2016
Earlier work this paper cites.
Y. Gao, F. Toni, H. Wang, and F. Xu, “Argumentation-based multi-agent decision making with privacy preserved,” in
2016
Earlier work this paper cites.
N. Schneider, N. Stiefl, and G. A. Landrum, “What’s what: The (nearly) definitive guide to reaction role assignment,”
2016
Earlier work this paper cites.
B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman, “Building machines that learn and think like people,”
2017
Earlier work this paper cites.
I. Donadello, L. Serafini, and A. d’Avila Garcez, “Logic tensor networks for semantic image interpretation,” in
2017
Earlier work this paper cites.
E. Parisotto, A.-r. Mohamed, R. Singh, L. Li, D. Zhou, and P. Kohli, “Neuro-symbolic program synthesis,” in
2017
Earlier work this paper cites.
C. Liang, J. Berant, Q. Le, K. Forbus, and N. Lao, “Neural symbolic machines: Learning semantic parsers on freebase with weak supervision,” in
2017
Earlier work this paper cites.
L. Mou, Z. Lu, H. Li, and Z. Jin, “Coupling distributed and symbolic execution for natural language queries,” in
2017
Earlier work this paper cites.
F. Yang, Z. Yang, and W. W. Cohen, “Differentiable learning of logical rules for knowledge base reasoning,” in
2017
Earlier work this paper cites.
T. Rocktäschel and S. Riedel, “End-to-end differentiable proving,” in
2017
Earlier work this paper cites.
M. Allamanis, P. Chanthirasegaran, P. Kohli, and C. Sutton, “Learning continuous semantic representations of symbolic expressions,” in
2017
Earlier work this paper cites.
J. Johnson, B. Hariharan, L. Van Der Maaten, J. Hoffman, L. Fei-Fei, C. Lawrence Zitnick, and R. Girshick, “Inferring and executing programs for visual reasoning,” in
2017
Earlier work this paper cites.
I. Donadello, L. Serafini, and A. d’Avila Garcez, “Logic tensor networks for semantic image interpretation,” in
2017
Earlier work this paper cites.
R. Stewart and S. Ermon, “Label-free supervision of neural networks with physics and domain knowledge,” in
2017
Earlier work this paper cites.
M. Diligenti, M. Gori, and C. Sacca, “Semantic-based regularization for learning and inference,”
2017
Earlier work this paper cites.
J. Cai, R. Shin, and D. Song, “Making neural programming architectures generalize via recursion,” in
2017
Earlier work this paper cites.
M. H. Segler and M. P. Waller, “Neural-symbolic machine learning for retrosynthesis and reaction prediction,”
2017
Earlier work this paper cites.
M. Balog, A. Gaunt, M. Brockschmidt, S. Nowozin, and D. Tarlow, “Deepcoder: Learning to write programs,” in
2017
Earlier work this paper cites.
R. Hu, J. Andreas, M. Rohrbach, T. Darrell, and K. Saenko, “Learning to reason: End-to-end module networks for visual question answering,” in
2017
Earlier work this paper cites.
K. Atkinson, P. Baroni, M. Giacomin, A. Hunter, H. Prakken, C. Reed, G. Simari, M. Thimm, and S. Villata, “Towards artificial argumentation,”
2017
Earlier work this paper cites.
B. Liu, B. Ramsundar, P. Kawthekar, J. Shi, J. Gomes, Q. Luu Nguyen, S. Ho, J. Sloane, P. Wender, and V. Pande, “Retrosynthetic reaction prediction using neural sequence-to-sequence models,”
2017
Earlier work this paper cites.
F. Xia, P. Wang, X. Chen, and A. L. Yuille, “Joint multi-person pose estimation and semantic part segmentation,” in
2017
Earlier work this paper cites.
Y. Wang, X. Liu, and S. Shi, “Deep neural solver for math word problems,” in
2017
Earlier work this paper cites.
G. Marcus, “Deep learning: A critical appraisal,”
2018
Earlier work this paper cites.
K. Yi, J. Wu, C. Gan, A. Torralba, P. Kohli, and J. B. Tenenbaum, “Neural-symbolic VQA: Disentangling reasoning from vision and language understanding,” in
2018
Cited alongside, same era.
L. Valkov, D. Chaudhari, A. Srivastava, C. Sutton, and S. Chaudhuri, “Houdini: Lifelong learning as program synthesis,” in
2018
Cited alongside, same era.
F. Yang, D. Lyu, B. Liu, and S. Gustafson, “PEORL: Integrating symbolic planning and hierarchical reinforcement learning for robust decision-making,” in
2018
Cited alongside, same era.
R. Manhaeve, S. Dumancic, A. Kimmig, T. Demeester, and L. D. Raedt, “Deepproblog: neural probabilistic logic programming,” in
2018
Cited alongside, same era.
H. Dai, Y. Tian, B. Dai, S. Skiena, and L. Song, “Syntax-directed variational autoencoder for structured data,” in
2018
Cited alongside, same era.
N. Gupta, K. Lin, D. Roth, S. Singh, and M. Gardner, “Neural module networks for reasoning over text,” in
2020
Later among the works it cites.
R. Saqur and K. Narasimhan, “Multimodal graph networks for compositional generalization in visual question answering,” in
2020
Later among the works it cites.
J. Zhang, L. Wang, R. K.-W. Lee, Y. Bin, Y. Wang, J. Shao, and E.-P. Lim, “Graph-to-tree learning for solving math word problems,” in
2020
Later among the works it cites.
A. Paliwal, S. Loos, M. Rabe, K. Bansal, and C. Szegedy, “Graph representations for higher-order logic and theorem proving,” in
2020
Later among the works it cites.
R. Riveret, S. Tran, and A. d. Garcez, “Neuro-symbolic probabilistic argumentation machines,” in
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Chen, C. Liu, and D. Song, “Tree-to-tree neural networks for program translation,” in
2018
Cited alongside, same era.
W. Jin, R. Barzilay, and T. Jaakkola, “Junction tree variational autoencoder for molecular graph generation,” in
2018
Cited alongside, same era.
F. Arabshahi, S. Singh, and A. Anandkumar, “Combining symbolic expressions and black-box function evaluations in neural programs,” in
2018
Cited alongside, same era.
R. Evans and E. Grefenstette, “Learning explanatory rules from noisy data,”
2018
Cited alongside, same era.
L. Zhang, G. Rosenblatt, E. Fetaya, R. Liao, W. Byrd, M. Might, R. Urtasun, and R. Zemel, “Neural guided constraint logic programming for program synthesis,” in
2018
Cited alongside, same era.
X. Liang, Z. Hu, H. Zhang, L. Lin, and E. P. Xing, “Symbolic graph reasoning meets convolutions,” in
2018
Cited alongside, same era.
J. Xu, Z. Zhang, T. Friedman, Y. Liang, and G. Broeck, “A semantic loss function for deep learning with symbolic knowledge,” in
2018
Cited alongside, same era.
X. Zhang, Y. Chen, B. Zhu, J. Wang, and M. Tang, “Part-aware context network for human parsing,” in
2020
Later among the works it cites.
A. Eberhart, M. Ebrahimi, L. Zhou, C. Shimizu, and P. Hitzler, “Completion reasoning emulation for the description logic el+,”
2020
Later among the works it cites.
Y. Yang, Y. Zhuang, and Y. Pan, “Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies,”
2021
Later among the works it cites.
J. Zhang, B. Chen, L. Zhang, X. Ke, and H. Ding, “Neural, symbolic and neural-symbolic reasoning on knowledge graphs,”
2021
Later among the works it cites.
T. Zhou, S. Qi, W. Wang, J. Shen, and S.-C. Zhu, “Cascaded parsing of human-object interaction recognition,”
2021
Later among the works it cites.
W. Wang, T. Zhou, S. Qi, J. Shen, and S.-C. Zhu, “Hierarchical human semantic parsing with comprehensive part-relation modeling,”
2021
Later among the works it cites.
F. Arabshahi, J. Lee, M. Gawarecki, K. Mazaitis, A. Azaria, and T. Mitchell, “Conversational neuro-symbolic commonsense reasoning,” in
2021
Later among the works it cites.
L. C. Lamb, A. d’Avila Garcez, M. Gori, M. O. Prates, P. H. Avelar, and M. Y. Vardi, “Graph neural networks meet neural-symbolic computing: A survey and perspective,” in
2021
Later among the works it cites.
D. Yu, B. Yang, D. Liu, and H. Wang, “A survey on neural-symbolic systems,”
2021
Later among the works it cites.
G. Booch, F. Fabiano, L. Horesh, K. Kate, J. Lenchner, N. Linck, A. Loreggia, K. Murgesan, N. Mattei, F. Rossi
2021
Later among the works it cites.
E. Tsamoura, T. Hospedales, and L. Michael, “Neural-symbolic integration: A compositional perspective,” in
2021
Later among the works it cites.
G. Marra and O. Kuželka, “Neural markov logic networks,”
2021
Later among the works it cites.
K. Marino, X. Chen, D. Parikh, A. Gupta, and M. Rohrbach, “Krisp: Integrating implicit and symbolic knowledge for open-domain knowledge-based vqa,” in
2021
Later among the works it cites.
E. Giunchiglia and T. Lukasiewicz, “Multi-label classification neural networks with hard logical constraints,”
2021
Later among the works it cites.
M. Asai and C. Muise, “Learning neural-symbolic descriptive planning models via cube-space priors: the voyage home (to strips),” in
2021
Later among the works it cites.
X. Lu, P. West, R. Zellers, R. Le Bras, C. Bhagavatula, and Y. Choi, “NeuroLogic decoding: (un)supervised neural text generation with predicate logic constraints,” in
2021
Later among the works it cites.
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko
2021
Later among the works it cites.
M. Sacha, M. Błaz, P. Byrski, P. Dabrowski-Tumanski, M. Chrominski, R. Loska, P. Włodarczyk-Pruszynski, and S. Jastrzebski, “Molecule edit graph attention network: modeling chemical reactions as sequences of graph edits,”
2021
Later among the works it cites.
R. Mukherjee, Y. Wen, D. Chaudhari, T. Reps, S. Chaudhuri, and C. Jermaine, “Neural program generation modulo static analysis,” in
2021
Later among the works it cites.
P. Verga, H. Sun, L. B. Soares, and W. Cohen, “Adaptable and interpretable neural memory over symbolic knowledge,” in
2021
Later among the works it cites.
A. Bosselut, R. Le Bras, and Y. Choi, “Dynamic neuro-symbolic knowledge graph construction for zero-shot commonsense question answering.” in
2021
Later among the works it cites.
K. Ma, F. Ilievski, J. Francis, Y. Bisk, E. Nyberg, and A. Oltramari, “Knowledge-driven data construction for zero-shot evaluation in commonsense question answering,” in
2021
Later among the works it cites.
J. Sun, H. Sun, T. Han, and B. Zhou, “Neuro-symbolic program search for autonomous driving decision module design,” in
2021
Later among the works it cites.
Y. Zhu, J. Tremblay, S. Birchfield, and Y. Zhu, “Hierarchical planning for long-horizon manipulation with geometric and symbolic scene graphs,” in
2021
Later among the works it cites.
X. Lin, Z. Huang, H. Zhao, E. Chen, Q. Liu, H. Wang, and S. Wang, “Hms: A hierarchical solver with dependency-enhanced understanding for math word problem,” in
2021
Later among the works it cites.
J. Qin, X. Liang, Y. Hong, J. Tang, and L. Lin, “Neural-symbolic solver for math word problems with auxiliary tasks,” in
2021
Later among the works it cites.
L. Von Rueden, S. Mayer, K. Beckh, B. Georgiev, S. Giesselbach, R. Heese, B. Kirsch, J. Pfrommer, A. Pick, R. Ramamurthy
2021
Later among the works it cites.
S.-W. Seo, Y. Y. Song, J. Y. Yang, S. Bae, H. Lee, J. Shin, S. J. Hwang, and E. Yang, “Gta: Graph truncated attention for retrosynthesis,” in
2021
Later among the works it cites.
X. Wang, Y. Li, J. Qiu, G. Chen, H. Liu, B. Liao, C.-Y. Hsieh, and X. Yao, “Retroprime: A diverse, plausible and transformer-based method for single-step retrosynthesis predictions,”
2021
Later among the works it cites.
R. Sun, H. Dai, L. Li, S. Kearnes, and B. Dai, “Towards understanding retrosynthesis by energy-based models,” in
2021
Later among the works it cites.
V. R. Somnath, C. Bunne, C. Coley, A. Krause, and R. Barzilay, “Learning graph models for retrosynthesis prediction,” in
2021
Later among the works it cites.
2021
Later among the works it cites.
I. Stepin, J. M. Alonso, A. Catala, and M. Pereira-Fariña, “A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence,”
2021
Later among the works it cites.
W. Wang, T. Zhou, F. Yu, J. Dai, E. Konukoglu, and L. Van Gool, “Exploring cross-image pixel contrast for semantic segmentation,” in
2021
Later among the works it cites.
J. Kim, P. Ravikumar, J. Ainslie, and S. Ontanon, “Improving compositional generalization in classification tasks via structure annotations,” in
2021
Later among the works it cites.
S. Ji, S. Pan, E. Cambria, P. Marttinen, and S. Y. Philip, “A survey on knowledge graphs: Representation, acquisition, and applications,”
2021
Later among the works it cites.
P. Smolensky, R. T. McCoy, R. Fernandez, M. Goldrick, and J. Gao,
2022
Closest in time.
P. Smolensky, R. McCoy, R. Fernandez, M. Goldrick, and J. Gao, “Neurocompositional computing: From the central paradox of cognition to a new generation of ai systems,”
2022
Closest in time.
T. R. Besold, A. d’Avila Garcez, S. Bader, H. Bowman, P. Domingos, P. Hitzler, K.-U. Kühnberger, L. C. Lamb, P. M. V. Lima, L. de Penning
2022
Closest in time.
L. Li, T. Zhou, W. Wang, J. Li, and Y. Yang, “Deep hierarchical semantic segmentation,” in
2022
Closest in time.
E. Giunchiglia, M. C. Stoian, and T. Lukasiewicz, “Deep learning with logical constraints,” in
2022
Closest in time.
——, “Deep learning with logical constraints,” in
2022
Closest in time.
P. Hitzler, A. Eberhart, M. Ebrahimi, M. K. Sarker, and L. Zhou, “Neuro-symbolic approaches in artificial intelligence,”
2022
Closest in time.
H. Kautz, “The third AI summer: AAAI Robert s. Engelmore memorial lecture,”
2022
Closest in time.
M. Jin, Z. Ma, K. Jin, H. H. Zhuo, C. Chen, and C. Yu, “Creativity of ai: Automatic symbolic option discovery for facilitating deep reinforcement learning,” in
2022
Closest in time.
N. Hoernle, R. M. Karampatsis, V. Belle, and K. Gal, “Multiplexnet: Towards fully satisfied logical constraints in neural networks,” in
2022
Closest in time.
K. Ahmed, S. Teso, K.-W. Chang, G. Van den Broeck, and A. Vergari, “Semantic probabilistic layers for neuro-symbolic learning,” in
2022
Closest in time.
S. Badreddine, A. d. Garcez, L. Serafini, and M. Spranger, “Logic tensor networks,”
2022
Closest in time.
E. van Krieken, E. Acar, and F. van Harmelen, “Analyzing differentiable fuzzy logic operators,”
2022
Closest in time.
X. Lu, S. Welleck, P. West, L. Jiang, J. Kasai, D. Khashabi, R. Le Bras, L. Qin, Y. Yu, R. Zellers, N. A. Smith, and Y. Choi, “NeuroLogic A*esque decoding: Constrained text generation with lookahead heuristics,” in
2022
Closest in time.
A. Tseng, J. J. Sun, and Y. Yue, “Automatic synthesis of diverse weak supervision sources for behavior analysis,” in
2022
Closest in time.
S. Ravishankar, J. Thai, I. Abdelaziz, N. Mihindukulasooriya, T. Naseem, P. Kapanipathi, G. Rossiello, and A. Fokoue, “A two-stage approach towards generalization in knowledge base question answering,” in
2022
Closest in time.
X. Ye, S. Yavuz, K. Hashimoto, Y. Zhou, and C. Xiong, “Rng-kbqa: Generation augmented iterative ranking for knowledge base question answering,” in
2022
Closest in time.
T. Silver, A. Athalye, J. B. Tenenbaum, T. Lozano-Perez, and L. P. Kaelbling, “Learning neuro-symbolic skills for bilevel planning,” in
2022
Closest in time.
Z. Li, W. Zhang, C. Yan, Q. Zhou, C. Li, H. Liu, and Y. Cao, “Seeking patterns, not just memorizing procedures: Contrastive learning for solving math word problems,” in
2022
Closest in time.
A. Fawzi, M. Balog, A. Huang, T. Hubert, B. Romera-Paredes, M. Barekatain, A. Novikov, F. J. R Ruiz, J. Schrittwieser, G. Swirszcz
2022
Closest in time.
T. Dash, S. Chitlangia, A. Ahuja, and A. Srinivasan, “A review of some techniques for inclusion of domain-knowledge into deep neural networks,”
2022
Closest in time.
M. Krenn, R. Pollice, S. Y. Guo, M. Aldeghi, A. Cervera-Lierta, P. Friederich, G. dos Passos Gomes, F. Häse, A. Jinich, A. Nigam
2022
Closest in time.
T. Zhou, W. Wang, E. Konukoglu, and L. Van Gool, “Rethinking semantic segmentation: A prototype view,” in
2022
Closest in time.
B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” in
2022
Closest in time.
C. Liang, W. Wang, J. Miao, and Y. Yang, “Gmmseg: Gaussian mixture based generative semantic segmentation models,” in
2022
Closest in time.
C. Liang, W. Wang, T. Zhou, and Y. Yang, “Visual abductive reasoning,” in
2022
Closest in time.
T. Gupta and A. Kembhavi, “Visual programming: Compositional visual reasoning without training,” in
2023
Closest in time.
Y. Shen, K. Song, X. Tan, D. Li, W. Lu, and Y. Zhuang, “Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface,” in
2023
Closest in time.
D. Surís, S. Menon, and C. Vondrick, “Vipergpt: Visual inference via python execution for reasoning,” in
2023
Closest in time.
H. Zhang, M. Dang, N. Peng, and G. Van den Broeck, “Tractable control for autoregressive language generation,” in
2023
Closest in time.
K. Ahmed, K.-W. Chang, and G. Van den Broeck, “A pseudo-semantic loss for autoregressive models with logical constraints,” in
2023
Closest in time.
L. Li, W. Wang, and Y. Yang, “Logicseg: Parsing visual semantics with neural logic learning and reasoning,” in
2023
Closest in time.
E. Giunchiglia, M. C. Stoian, S. Khan, F. Cuzzolin, and T. Lukasiewicz, “Road-R: The autonomous driving dataset with logical requirements,”
2023
Closest in time.
W. Zhong, Z. Yang, and C. Y.-C. Chen, “Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing,”
2023
Closest in time.
Z. Li, Y. Yao, T. Chen, J. Xu, C. Cao, X. Ma, L. Jian
2023
Closest in time.
M. Proietti and F. Toni, “A roadmap for neuro-argumentative learning,” in
2023
Closest in time.
J. Hsu, J. Mao, and J. Wu, “Ns3d: Neuro-symbolic grounding of 3d objects and relations,” in
2023
Closest in time.
J. Jiang, F. Leofante, A. Rago, and F. Toni, “Formalising the robustness of counterfactual explanations for neural networks,” in
2023
Closest in time.
C. Liang, W. Wang, J. Miao, and Y. Yang, “Logic-induced diagnostic reasoning for semi-supervised semantic segmentation,” in
2023
Closest in time.
J. Liang, T. Zhou, D. Liu, and W. Wang, “Clustseg: Clustering for universal segmentation,” in
2023
Closest in time.
E. van Krieken, T. Thanapalasingam, J. Tomczak, F. Van Harmelen, and A. Ten Teije, “A-nesi: A scalable approximate method for probabilistic neurosymbolic inference,” in
2023
Closest in time.
J. Maene and L. De Raedt, “Soft-unification in deep probabilistic logic,” in
2023
Closest in time.
W. Wang, Y. Yang, and Y. Pan, “Visual knowledge in the big model era: Retrospect and prospect,”
2024
Closest in time.
E. Giunchiglia, A. Tatomir, M. C. Stoian, and T. Lukasiewicz, “CCN+: A neuro-symbolic framework for deep learning with requirements,”
2024
Closest in time.
H. Monte-Alto, M. Morveli-Espinoza, and C. Tacla, “Argumentation-based multi-agent distributed reasoning in dynamic and open environments,”
2024
Closest in time.
Z. Li, Y. Huang, Z. Li, Y. Yao, J. Xu, T. Chen, X. Ma, and J. Lu, “Neuro-symbolic learning yielding logical constraints,” in
2024
Closest in time.
Z. Yang, G. Chen, X. Li, W. Wang, and Y. Yang, “Doraemongpt: Toward understanding dynamic scenes with large language models (exemplified as a video agent),” in
2024
Closest in time.